Data Systems Engineer at CHEQ, building high-throughput, reliable data infrastructure and analytics tools in the cloud. Also an Adjunct Professor at Loyola University Chicago, teaching introductory computer science courses.
- Designing event-driven data pipelines for large-volume telemetry and analytics workloads
- Creating modular cloud templates for reproducible environments and faster delivery
- Developing tools for data quality, validation, and observability across distributed systems
- Leading a generative-AI integration to reduce configuration friction and improve customer onboarding and power-user workflows
- ASE 2022 Tool Demo (co-author): “Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics” (ASE profile)
- Graduate poster: Toward a Containerized Pipeline for Longitudinal Analysis of Open-Source Software Projects — Loyola Graduate Research Symposium (2020)
- Declarative data validation (Pandera, Pydantic v2) to enforce schema guarantees in ETL/ELT
- Composable Lambda + orchestrations for lightweight, maintainable serverless patterns
Languages: Python, SQL, Java Spring, TypeScript Infra: AWS (SAM, Step Functions, Athena, QuickSight), Devbox Data tools: ClickHouse, Pandera, Pydantic, Pandas Teaching: Python, Linux, Git, Algorithmic Thinking
📧 Email: amiller17@luc.edu 💼 LinkedIn: linkedin.com/in/ajm10565 🐙 GitHub: github.com/ajm10565